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Object-based audio content is becoming popular to represent immersive spatial sound because audio object positions can be totally independent of the locations of available loudspeakers. The encoder of the spatial audio object coding (SAOC) method transmits a mono downmix signal with spatial parameters of all audio objects, and the decoder reconstructs the original audio objects. However, the decoded audio object by SAOC has severe aliasing distortion, especially at low bitrates. This study focuses on distortion reduction of decoded audio objects at low bitrates. We present a new audio coding method via nonuniform fine sub-bands division strategy with convolutional autoencoder and a dense convolutional network mixture model, aiming to effectively compress spatial parameters of all audio objects. Evaluations indicate that the proposed approach reduces the aliasing distortion by 2.02%. It has achieved the best performances in both objective and subjective results with a low bitrate 1 kb/s per object. [ABSTRACT FROM AUTHOR]

The rapid development and popularization of video surveillance highlight the critical and challenging problem, vehicle reidentification, which suffers from the limited interinstance discrepancy between different vehicle identities and large intrainstance differences of the same vehicle. In this article, we propose a novel multilevel attention network to hierarchically learn an efficient feature embedding for vehicle re-ID. Three kinds of attention are designed in the network: hard local-level attention to localize vehicle salient parts, soft pixel-level attention to refine attended pixels both globally and locally, and spatial attention to enhance the encoder's spatial awareness of salient regions within the windscreen area. Multigrain features are subsequently learned from semantic awareness to spatial awareness, guaranteeing the intraclass compactness and interclass separability for vehicle re-ID. Extensive experiments validate the effectiveness of each attention component and demonstrate that our approach outperforms the state-of-the-art re-ID methods on two challenging datasets: VehicleID and Vehicle-1 M. [ABSTRACT FROM AUTHOR]

In this paper, we propose a face super-resolution (FSR) method to handle the decreasing face recognition rate caused by low-quality images. To better model the input images, we build a nearest neighbor network (NNN) which consists of nodes and paths by introducing the second-layer nearest neighbors (SLNNs), where the paths of the network represent the distance between nodes. As the SLNN is trained in the high-resolution (HR) space and is exponentially supplementary to the traditional first-layer nearest neighbors (FLNNs), the neighbor inadequacy problem can be effectively solved by enriching the neighbor candidate set via NNN. Furthermore, we solve the NNN for the optimal weights of neighbors. Finally, we fuse the refined weights and neighbors for better reconstruction results. The effectiveness of this fusion strategy is validated by both quantitative and qualitative experimental results. The extensive experimental results on the public face datasets and real-world challenging low-resolution (LR) images demonstrate that the proposed method performs favorably against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Huge earth observatory video data (EOVD) and the limited transmission bandwidth from satellites to terrestrial devices pose serious challenges to compression efficiency for satellite video. In this work, we deeply explore long-term background redundancy caused by periodical satellite revisit, and make utilization of long-term background reference (LTBR) to design a high-efficiency coding method specific for EOVD. Firstly, we use data of Google Earth as prior background knowledge and construct LTBR from it. Then, two novel prediction methods are proposed to make full use of prior information of LTBR, including color and definition correction based inter prediction with LTBR (CDIP-LTBR), structure and texture constrained intra prediction with LTBR (STIP-LTBR). Lastly, the proposed two new prediction schemes are integrated into one unified coding framework along with HEVC to achieve improved coding performance, and an improved RDO method is designed for additional prediction modes selection problem in the framework to obtain a higher prediction efficiency. Extensive experiments on real-world EOVD show that the proposed coding scheme exhibit significant improvement over HEVC and H.264, in terms of BD-Rate, BD-PSNR and rate distortion comparison. [ABSTRACT FROM AUTHOR]

By developing sophisticated image priors or designing deep(er) architectures, a variety of image super-resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a maximum a posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Especially, we introduce a reference dataset, which is composed of high-resolution (HR) and low-resolution (LR) image pairs, to measure the SR abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR estimation should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art nondeep learning-based methods, four latest deep learning-based methods, and one ensemble learning-based method, and prove its effectiveness and superiority on some general image datasets and face image datasets. [ABSTRACT FROM AUTHOR]

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Correlation filters (CFs) have been extensively used in tracking tasks due to their high efficiency although most of them regard the tracked target as a whole and are minimally effective in handling partial occlusion. In this study, we incorporate a part-based strategy into the framework of CFs and propose a novel multipart correlation tracker with triangle-structure constraints. Specifically, we train multiple CFs for the global object and local parts, which are then jointly applied to obtain the correlation response of any candidate during tracking. The tracker is robust in handling partial occlusion because of the use of part-based representation. The remaining global representation can contribute reliable cues in cases wherein several local filters drift away in a specific scene. We further propose a triangle-structure model to measure the structural similarity of candidates. The model employs multiple triangles to determine the spatial relationship among parts and helps constrain the location of the target. Moreover, we introduce an effective part selection scheme based on energy and integrity, which is generally applicable to part-tracking models. Extensive experiments on two public benchmarks demonstrate the superiority of the proposed method over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]